Material identification during turning by neural network
More details
Hide details
Institute of Production Engineering and Machine Tools (IFW), Leibniz Universität Hannover, Germany
Submission date: 2020-01-10
Acceptance date: 2020-02-25
Online publication date: 2020-06-24
Publication date: 2020-06-24
Journal of Machine Engineering 2020;20(2):65–76
A design concept for high-performance components involves the combination of different materials in hybrid workpieces. Different material properties and chemical compositions influence the machining quality of hybrid workpieces. To achieve a constant workpiece and process quality, it is necessary to adjust the process parameters to the individual material. Thus, it is mandatory to classify the material during machining for the relevant range of process parameters. This paper examines teaching strategies for neural networks to determine the machined material in process by a small amount of cross points. For this purpose, different training sets are compared. Process parameters with different cutting speeds, feeds and with constant and varying depth of cut are examined. In addition, the signal sources necessary for robust material classification are compared and investigated. The investigation is performed for the cylindrical turning of friction welded EN AW-6082/20MnCr5 shafts. The study shows that an F1 score of 0.99 is achieved at a constant cutting depth, provided that only the corner points of the process window and the machine control signals are used for training. With an additional variation of the cutting depth, the classification rate is significantly improved by the use of external sensors such as the acceleration sensor.
The results presented in this paper were obtained within the Collaborative Research Centre 1153 ‘‘Process chain to produce hybrid high performance components by Tailored Forming’’ in the subproject B5 (project number: 252662854). The authors would like to thank the German Research Foundation (DFG) for the financial and organizational support of this project.
GOEDE M., STEHLIN M., RAFFLENBEUL L., KOPP G., BEEH E., 2009, Super light car-lightweight construction thanks to a multi-material design and function integration, European Transport Research Review, 1/1, 5–10.
BEHRENS B-A., BOUGUECHA A., FRISCHKORN C., HUSKIC A., STAKHIEVA.A., DURAN D., 2016, Tailored Forming technology for three dimensional components: approaches to heating and forming, 5th Conference on Thermomechanical Processing, Milan, Italy.
BEHRENS B-A., BOUGUECHA A., VUCETIC M., PESHEKHODOV I., MATTHIAS T., KOLBASNIKOV N., SOKOLOV S., GANIN S., 2016, Experimental investigations on the state of the friction-welded joint zone in steel hybrid components after process-relevant thermo-mechanical, AIP Conf. Proc., 1769.
BLOHM T., MILDEBRATH M., STONIS M., LANGNER J., HASSEL T., BEHRENS B-A., 2017, Investigation of the coating thickness of plasma-transferred arc deposition welded and cross wedge rolled hybrid part, Production Engineering Research and Development, 11/3, 244–263.
GOLDSTEIN R., BEHRENS B-A., DURAN D., 2017, Lightweighting by Tailored Forming: bi-material stepped shaft, Heat Treat 2017, Proceedings of the 29th ASM Heat Treating Society Conference, October 24-26, Columbus, Ohio, USA.
BOEHNKE D., 2007, Qualitätsorientierte Zerspanung von Parallelverbunden im kontinuierlichen Schnitt, Dissertation, Leibniz Universität Hannover.
OZSVÁTH P., SZMEJKÁL A., TAKÁCS J., EIDENHAMMER M., OBERMAIR F., 2006, Development of face milling process for Mg-hybrid (Mg-Al, Mg-sintered steel) materials, Proceedings of the 7th Int. Conference on Magnesium Alloys and Their Applications, Wiley-VCH, 894–900.
DENKENA B., BERGMANN B., BREIDENSTEIN B., PRASANTHAN V., WITT M., 2018, Analysis of potentials to improve the machining of hybrid workpieces, Production Engineering Research and Development, 13/1, 11–19.
GE Z., SONG Z., DING S., HUANG B., 2017, Data Mining and analytics in the process industry: The Role of Machine Learning, IEEE Access, 5, 20590–20616.
WUEST T., WEIMER D., IRGENS C., THOBEN K., 2016, Machine learning in manufacturing: advantages, challenges, and applications, Production & Manufacturing Research, 4/1, 23–45.
KILUNDU B., DEHOMBREUX P., CHIEMENTIN X., 2011, Tool wear monitoring by machine learning techniques and singular spectrum analysis, Mechanical Systems and Signal Processing, 25, 400–415.
LAMRAOUI M., BARAKAT M., THOMAS M., BADAOUI M.E., 2013, Chatter detection in milling machines by neural network classification and feature selection, J. Vib. Control, 21, 1251–1266.
DENKENA B., BERGMANN B., WITT M., 2018, Material identification based on machine learning algorithms for hybrid workpieces during cylindrical operations, Journal of Intelligent Manufacturing, 30/6, 2449–2456.